self-improving-agent

Self-Improving Agent - Autonomous Learning Patterns

Architectural patterns for AI agents that get better with use. Most agents are stateless -- they repeat mistakes because they cannot learn from their own execution. This skill closes that gap with patterns for feedback capture, memory curation, skill extraction, and regression detection. Key insight: auto-memory captures everything, but curation turns noise into knowledge.

Core Capabilities

• Memory curation — a layered memory stack (CLAUDE.md → MEMORY.md → session), review protocol, and promotion criteria for graduating learnings into enforced rules.
• Feedback loops — outcome classification, signal extraction, and a capture template that turn every task result into a structured learning.
• Regression detection — metrics, thresholds, and a response protocol that flags performance degradation within a few sessions.
• Skill extraction — criteria and a 4-step process to graduate proven patterns into standalone skill packages.
• Meta-learning — adaptive capture strategy and anti-pattern detection so the agent learns what is worth learning.
• Continuous calibration — confidence scoring and belief revision for resolving contradictions across learned knowledge.

When to Use

• Building agents intended to improve over time rather than stay stateless.
• Managing auto-memory (MEMORY.md) and deciding what to keep, promote, or retire.
• Designing self-correcting feedback loops and regression alarms for agent behavior.
• Graduating recurring solutions into reusable skill packages.

Clarify First

Before capturing or promoting learnings, settle the inputs below. When one is missing or vague and would change the result, ask about it; when it wouldn't, use a sensible default and say which one you used.

• Loop stage: remember / extract / promote / review (routes the sub-skill and the whole workflow).
• Source data: which session logs, MEMORY.md, and rules dir to operate on (the subject the tools read and write).
• Promotion bar: min occurrences / confidence threshold for graduating a learning into an enforced rule (--min-occurrences; decides what is kept vs discarded).

Ask about the two or three inputs that matter most for this request, not the whole list. If the user wants a result straight away, produce it and state your assumptions at the top.

Sub-Skills

Compound sub-skill architecture — each file in skills/ handles one step of the improvement loop:

Sub-Skill	File	Purpose
Remember	skills/remember.md	Capture errors and learnings from current session
Extract	skills/extract.md	Extract reusable patterns from completed work
Promote	skills/promote.md	Graduate proven patterns to permanent rules
Review	skills/review.md	Audit memory health, prune stale entries
Status	skills/status.md	Dashboard showing memory state and learning progress

Flow: Remember → Extract → Promote → Review, with Status providing visibility back into the cycle.

Tools

Tool	Purpose	Command
pattern_extractor.py	Extract reusable patterns from session logs	python scripts/pattern_extractor.py --input sessions.jsonl --min-occurrences 3
memory_health_checker.py	Audit memory for line counts, stale, and promotable entries	python scripts/memory_health_checker.py --memory ./MEMORY.md --rules ./.claude/rules/
rule_promoter.py	Validate and apply promotions from memory to rules	python scripts/rule_promoter.py --memory ./MEMORY.md --list-candidates
feedback_analyzer.py	Analyze feedback logs for success rates and opportunities	python scripts/feedback_analyzer.py analyze
regression_detector.py	Compare baseline vs current performance metrics	python scripts/regression_detector.py compare
rule_manager.py	Manage a learned rules knowledge base with CRUD	python scripts/rule_manager.py list

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

• references/memory-curation-guide.md — the memory stack, review protocol, promotion criteria/targets, the Weekly Memory Health Check workflow, and the continuous-calibration (confidence scoring + belief revision) machinery. Read when curating MEMORY.md or promoting learnings to rules.
• references/feedback-loop-patterns.md — the core improvement-loop architecture and maturity levels, outcome classification + signal extraction, the capture template, regression metrics/response, the post-session and regression-investigation workflows, common pitfalls, troubleshooting, and the success-criteria bar. Read when designing feedback capture or diagnosing a regression.
• references/meta-learning-architectures.md — skill-extraction criteria and process, the adaptive capture strategy, and anti-pattern detection. Read when the agent should adapt its own learning strategy or extract a proven pattern into a skill.
• references/self-improvement-methodology.md — the five layers of agent learning, the confidence-scoring model, the promotion decision tree, the memory-curation checklist, anti-patterns, and the metrics/thresholds table. Read for the end-to-end methodology overview.

Scope & Limitations

This skill covers:

• Architectural patterns for building agents that learn from execution history and user feedback.
• Memory lifecycle management: capture, curation, promotion, and retirement of learned knowledge.
• Performance regression detection frameworks and response protocols for agent systems.
• Skill extraction methodology for graduating proven patterns into reusable, standalone packages.

This skill does NOT cover:

• Runtime agent orchestration or multi-agent coordination -- see agent-workflow-designer and agent-protocol.
• Prompt engineering, testing, or versioning of the prompts themselves -- see prompt-engineer-toolkit.
• Infrastructure-level observability (logging, tracing, alerting dashboards) -- see observability-designer.
• Initial agent architecture design, tool selection, or capability planning -- see agent-designer.

Integration Points

Skill	Integration	Data Flow
context-engine	Controls what the agent sees per session; this skill decides what is worth remembering long-term	Promoted rules and curated memory feed context retrieval; context relevance metrics flow back for regression tracking
agent-designer	Defines the agent's architecture and capabilities; this skill layers learning infrastructure on top	Architecture constraints inform possible feedback loops; extracted skills feed back as new capabilities
prompt-engineer-toolkit	Prompts degrade as codebases evolve; this skill detects prompt regression via outcome tracking	Performance metrics flag underperforming prompts; prompt updates feed back as CLAUDE.md rule changes
observability-designer	Provides system-level metrics; this skill provides agent-behavior-level metrics	System telemetry enriches regression diagnosis; agent metrics export to observability dashboards
tech-debt-tracker	Stale rules and bloated memory are technical debt this can surface alongside code debt	Memory health metrics feed debt scoring; debt prioritization informs which stale rules to retire
agent-workflow-designer	Multi-step workflows benefit from per-step feedback capture and cross-workflow pattern extraction	Per-step outcome data flows into feedback loops; extracted optimizations update workflow definitions